安装mgarch包终极解决方案

Mon, 10 Jun 2013 00:00:00 +0000

mgarch包是一个可以拟合和诊断BEKK模型的R包。包中的主函数很少，只有mvBEKK.sim，mvBEKK.diag，mvBEKK.est三个。这三个函数各司其职，分别用来模拟BEKK数据、诊断BEKK模型、拟合BEKK模型。照常理说，这样的包很好啦。就只有一样不好，作者自从2009年更新一次之后，再也不提供更新的.zip包了。很多需要拟合BEKK模型的同学急得团团转就是不知道怎么安装和使用这个包。这里提供三个方案：

方案一：

下载mgarch的源代码包mgarch_0.00-1.tar.gz。解压缩到R的当前工作文件夹中。如果不知道当前工作文件夹路径。敲入get.wd()函数就可以看到了。解压缩之后，按照mgarch->>R的顺序打开文件夹，你会在R文件夹中看到好几个.r格式的文件。用source()函数载入进来就可以用了。

方案二：

拿着mgarch包，按照开发R程序包之忍者篇自己编译mgarch包。

方案三:

这个方案跟方案二类似，也得先安装Rtools，目的是可以编译源代码。同时，由于mgarch依赖于zoo、tseries、mvnorm三个支撑包，所以，也得捎带手安装一下，并保证它们被升级了到合适的版本。接着，将mgarch_0.00-1.tar.gz放到当前工作文件夹中（不知道当前工作文件夹是哪个？看方案一）。最后，敲入下面的代码：

install.packages("mgarch_0.00-1.tar.gz",repos=NULL,type="source") ##安装源代码文件
library(mgarch) ##查看是否可以使用

BEKK模型

Thu, 15 Apr 2010 00:00:00 +0000

BEKK模型是目前比较流行的多变量波动性模型，可以看作GARCH模型在多维空间的实现。BEKK模型在应用过程中也有诸多缺点，其中最大的一个不足之处在于模型的待估计参数会随着模型维数的增加指数型增长。然而，这一点肯定会随着计算机的发展而被逐渐克服的。

R软件中提供了mgarch和mgarchBEKK两个包来估计BEKK模型参数。

mgarch包及其示例

# 安装包（如果未安装）
# devtools::install_github("https://github.com/vst/mgarch")

library(mgarch)

## 载入需要的程序包：tseries

## Registered S3 method overwritten by 'quantmod':
## method from
## as.zoo.data.frame zoo

## 载入需要的程序包：mvtnorm

先看mgarch包，该包有三个主要函数：

mvBEKK.sim: 模拟一个MGARCH-BEKK过程
mvBEKK.est: 估计MGARCH-BEKK模型的参数
mvBEKK.diag:对拟合的MGARCH-BEKK模型进行诊断

可以通过以下命令查询相关函数的用法：

?mvBEKK.sim
?mvBEKK.est
?mvBEKK.diag

运行以下命令可以模拟一个MGARCH-BEKK过程：

set.seed(123)
sim = mvBEKK.sim(series.count = 3, T = 1000)

## Class attributes are accessible through following names:
## length series.count order params true.params eigenvalues uncond.cov.matrix white.noise eps cor sd

合并模拟的时间序列：

eps = data.frame(sim$eps[[1]], sim$eps[[2]], sim$eps[[3]]) 
head(eps)

## sim.eps..1.. sim.eps..2.. sim.eps..3..
## 1 0.9472441 1.2177306 0.5491724
## 2 -1.1241768 -0.3366113 -0.6261458
## 3 0.7179649 -0.3272567 1.6880032
## 4 -0.4857962 1.2230006 -1.9563961
## 5 -1.1301479 4.0603942 -1.3856283
## 6 0.2057734 1.1435079 -0.6392246

tail(eps)

## sim.eps..1.. sim.eps..2.. sim.eps..3..
## 995 -1.43678861 -0.8651118 -1.55380698
## 996 -1.10173653 -0.5863329 -1.14479754
## 997 -1.06902893 -0.4398771 -3.08558958
## 998 -0.11478218 0.7226409 -3.00375826
## 999 -1.18562636 2.4487154 -0.04579639
## 1000 0.05764529 -0.2241948 0.86390660

估计模型参数：

est <- mvBEKK.est(eps)

## H IS SINGULAR!...
## H IS SINGULAR!...
## H IS SINGULAR!...

## Warning in mvBEKK.est(eps): negative inverted hessian matrix element

查看估计结果包含哪些信息：

names(est)

## [1] "eps" "series.length" "estimation.time" 
## [4] "total.time" "order" "estimation" 
## [7] "aic" "asy.se.coef" "est.params" 
## [10] "cor" "sd" "H.estimated" 
## [13] "eigenvalues" "uncond.cov.matrix" "residuals"

模型诊断：

mvBEKK.diag(est)

## 	Number of estimated series : 3 
## 	Length of estimated series : 1000 
## 	Estimation Time : 32.41842 
## 	Total Time : 32.98894 
## 	BEKK order : 1 1 
## 	Eigenvalues : 0.8982855 0.5101535 0.4535976 0.3020472 0.2611067 0.2258987 0.1373383 0.05469751 0.05280793 
## 	aic : 4163.509 
## 	unconditional cov. matrix : 2.654942 -0.4582163 0.8566066 -0.4582163 2.38067 0.1093766 0.8566066 0.1093766 2.01 
## 	var(resid 1 ) : 0.999399 
## 	mean(resid 1 ) : -0.03860414 
## 	var(resid 2 ) : 0.9988757 
## 	mean(resid 2 ) : 0.04335019 
## 	var(resid 3 ) : 0.997348 
## 	mean(resid 3 ) : 0.02152967 
## 	Estimated parameters :
## 
## 	C estimates:
## [,1] [,2] [,3]
## [1,] 0.8909642 0.3328637 0.34152387
## [2,] 0.0000000 0.9827892 0.05270326
## [3,] 0.0000000 0.0000000 1.01236445
## 
## 	ARCH estimates:
## [,1] [,2] [,3]
## [1,] 0.340809760 0.06992293 0.07918491
## [2,] -0.064873614 0.36049917 0.35553210
## [3,] -0.008381618 -0.06665216 0.50150310
## 
## 	GARCH estimates:
## [,1] [,2] [,3]
## [1,] -0.3353086 0.37376137 -0.5032390
## [2,] 0.2151724 0.03756262 0.4608374
## [3,] 0.1756196 0.01577004 -0.4594647
## 
## 	asy.se.coef : 
## 
## 	C estimates, standard errors:
## [,1] [,2] [,3]
## [1,] 0.07738385 0.1351731 0.2359051
## [2,] 0.00000000 0.1141723 0.2671477
## [3,] 0.00000000 0.0000000 0.2311768
## 
## 	ARCH estimates, standard errors:
## [,1] [,2] [,3]
## [1,] 0.05481912 0.05484527 0.07796872
## [2,] 0.04677913 0.05338068 0.07267995
## [3,] 0.02304154 0.02555282 0.04677987
## 
## 	GARCH estimates, standard errors:
## [,1] [,2] [,3]
## [1,] 0.17105425 0.19123870 0.31018515
## [2,] 0.17509179 0.26972309 0.33665408
## [3,] 0.05380941 0.07028591 0.09210273
## Called from: mvBEKK.diag(est)
## debug: for (i in 1:length(estimation$eps)) {
## plot(estimation$residuals[[i]])
## browser()
## dev.off()
## }
## debug: plot(estimation$residuals[[i]])

## debug: browser()
## debug: dev.off()
## debug: plot(estimation$residuals[[i]])

## debug: browser()
## debug: dev.off()
## debug: plot(estimation$residuals[[i]])

## debug: browser()
## debug: dev.off()

mgarchBEKK包及其示例

# 安装包（如未安装）
# devtools::install_github("https://github.com/vst/mgarchBEKK")

# 加载包
library(mgarchBEKK)
# 模拟mgarchBEKK过程
simulated <- simulateBEKK(2, 1000, c(1,1))

## Class attributes are accessible through following names:
## length series.count order params true.params eigenvalues uncond.cov.matrix white.noise eps cor sd

# 合并模拟数据
simulated <- do.call(cbind, simulated$eps)
# 估计模型
estimated <- BEKK(simulated)

## H IS SINGULAR!...
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## Warning in BEKK(simulated): negative inverted hessian matrix element

# 模型诊断
diagnoseBEKK(estimated)

## 	Number of estimated series : 2000 
## 	Length of estimated series : 1000 
## 	Estimation Time : 3.208226 
## 	Total Time : 3.330154 
## 	BEKK order : 1 1 
## 	Eigenvalues : 0.7940983 0.7122278 0.5839173 0.5353965 
## 	aic : 4614.775 
## 	unconditional cov. matrix : 6.481396 1.496551 1.496551 7.939293 
## 	var(resid 1 ) : 1.000363 
## 	mean(resid 1 ) : -0.007109519 
## 	var(resid 2 ) : 0.9958192 
## 	mean(resid 2 ) : -0.01444951 
## 	Estimated parameters :
## 
## 	C estimates:
## [,1] [,2]
## [1,] 0.9304752 0.4214324
## [2,] 0.0000000 1.6921455
## 
## 	ARCH estimates:
## [,1] [,2]
## [1,] -0.5382996 0.003724502
## [2,] 0.3896205 -0.351431137
## 
## 	GARCH estimates:
## [,1] [,2]
## [1,] 0.3804035 0.5270529
## [2,] -0.2806881 0.6526116
## 
## 	asy.se.coef : 
## 
## 	C estimates, standard errors:
## [,1] [,2]
## [1,] 0.2069876 0.5373757
## [2,] 0.0000000 0.2889314
## 
## 	ARCH estimates, standard errors:
## [,1] [,2]
## [1,] 0.04044854 0.04641720
## [2,] 0.02825686 0.04050683
## 
## 	GARCH estimates, standard errors:
## [,1] [,2]
## [1,] 0.05265359 0.05895967
## [2,] 0.06856104 0.07941475

## Called from: diagnoseBEKK(estimated)
## debug: dev.off()
## debug: plot(estimation$residuals[[i]])

## debug: browser()
## debug: dev.off()